1. UNIVERSITÁ DEGLI STUDI DI SALERNO
Other more complex
feedback control structures
Prof. Ing. Michele MICCIO
Dip. Ingegneria Industriale (Università di Salerno)
Prodal Scarl (Fisciano)
rev. 2.1 of May 15, 2017

2. Adaptive control or Self-Tuning
Ingegneria e Tecnologia dei Sistemi di Controllo
Adaptive control or Self-Tuning
Lezione 2
see § 7.1.1
see § 22.1
A control technique in which one or more parameters of the PID controller are sensed and used to vary the feedback control signals in order to satisfy the performance criteria.
There are many situations where the changes in process dynamics are so large that a constant linear feedback controller will not work satisfactorily. For example, the dynamics of a supersonic aircraft.
Adaptive control is also useful for industrial process control. Since delay and holdup times depend on production, it is desirable to retune the regulators when there is a change in production.
Adaptive control can also be used to compensate for changes due to aging and wear.
G. Magnani

3. Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Inferential Control
 Problem:
The controlled variable cannot be measured (or has a too large sampling period).
Possible solutions:
Control another related variable (e.g., temperature instead of composition).
Inferential control:  Control is based on a suitable estimate of the controlled variable.
see § 22.2
 Seborg, Process_Dynamics_and_Control_2nd-ed_2003
G. Magnani

4. Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Inferential Control
see § 22.2
G. Magnani

5. Inferential Control Soft sensor
Ingegneria e Tecnologia dei Sistemi di Controllo
Inferential Control Soft sensor
Lezione 2
Soft sensor or virtual sensor is a common name for software where several measurements are processed together and then properly used for calculating new quantities, which need not be measured.
Soft sensors are used when hardware sensors are unavailable or unsuitable
Soft sensors are inferential estimators, taking advantage of available measurements and drawing conclusions from process observations
Well-known software algorithms that can be seen as soft sensors include Kalman filters.
More recent implementations of soft sensors use neural networks or fuzzy computing.
Examples:
Kalman filters for estimating the geographical location
Soft sensors in the biotech industry:
to determine total cell mass in a bioreactor
to estimate the concentration of product proteins inside microorganisms
 Seborg, Process_Dynamics_and_Control_2nd-ed_2003
 Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G., Soft Sensors for Monitoring and Control of Industrial Processes, 2007
G. Magnani

6. Robust control Robustness
Robust control aims at designing a fixed (non–adaptive) controller such that some defined level of performance of the controlled system (e.g., closed– loop stability, reference tracking performance and disturbance rejection performance) is guaranteed, irrespective of changes in plant dynamics within a predefined (typically compact) set
Robust control is a branch of control theory that explicitly deals with uncertainty in its approach to controller design.
The early methods of Bode and others were fairly robust; the theory of Robust Control took shape in the 1980s and 1990s and is still active today.
In contrast with an adaptive control policy, a robust control policy is static; rather than adapting to measurements of variations, the controller is designed to work assuming that certain variables will be unknown but, for example, bounded.

7. Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Cascade control d1 2
actual process
see § 8.2 at pag. 245
see § 20.1
at pag. 395
Two feedback control loops are "nested”:
primary or master or external loop
secondary or slave or internal loop
only one manipulated variable u, but more than one measured variable
w must be a measurable (auxiliary) variable
the output from the "master” controller R1 becomes the set point for the "slave” controller R2
usually, G2 is a minimum phase system
G1 can be a non-minimum phase system
G. Magnani

8. Cascade control Example of a Heat Exchanger with measuring delay
Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Cascade control Example of a Heat Exchanger with measuring delay
Secondary temperature T0 loop
See §8.2 in Magnani, Ferretti e Rocco
G. Magnani

9. Cascade control Example of a Cascade Jacketed Reactor
Controlled variables:
Reactor temperature
Coolant output temperature

10. Cascade control Example of a Cascade Jacketed Reactor
Two process measurements:
the primary or outer measured process variable is still the product stream temperature
the secondary measured process variable is coolant temperature out of the jacket
Two controllers
Only one final control element
Notes:
As with all cascade architectures, the output of the primary controller is the set point of the secondary controller.
The secondary or inner loop controller manipulates the cooling jacket flow rate.
 The benefit of a cascade architecture is improved disturbance rejection.

11. Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Ratio Control
see § at pag. 260
see § 21.5 at pag. 427
Definition
A control scheme the objective of which is to maintain the ratio of two variables at a pre-specified value. 
A ratio control system is characterized by the fact that variations in the secondary variable don’t reflect back on the primary variable.
G. Magnani

12. Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Ratio Control
Typical applications
see § at pag. 260
Mixing
Diluition
P&ID schemes
. . .
Holding the fuel-air ratio in a burner to the optimum.
Maintaining a stoichiometric ratio of reactants of a reactor.
Keeping a specified reflux ratio for a distillation column, etc. 
G. Magnani

13. Ratio Control Control strategy
see § at pag. 260
see § 21.5 at pag. 427
Ratio Control Control strategy
There are at least two possible ways to implement the ratio control strategy:
Ratio computation
"wild" stream A
stream B
where:
r = wB/wA
is the setpoint 
The flow rate of one of the streams feeding the mixed flow, designated as the wild feed, can change freely.
The ratio controller manipulates a second variable to maintain the desired ratio between the first and the second variable.
Set-point (wB) computation
where:
K = wB/wA
Introduction to Process Control Romagnoli & Palazoglu

14. Ingegneria e Tecnologia dei Sistemi di Controllo
Multivariable control
Lezione 2
Example: 2x2 (DIDO) open loop system
see § 8.6 at pag. 265
see ch. 23 and 24
G. Magnani

15. Ingegneria e Tecnologia dei Sistemi di Controllo
Lezione 2
Multivariable control
Example: 2x2 (DIDO) closed loop system
 Loop interaction may enhance instability
G. Magnani

16. Ingegneria e Tecnologia dei Sistemi di Controllo
Multivariable control
Lezione 2
Example of a DIDO system:
three way mixing valve
see § 8.6 at pag. 265
Controlled variables:
output flowrate
output composition
P&ID scheme
 Loops are separated:
no loop interaction!
G. Magnani

17. Multivariable control
Example of a DIDO system:
Binary Distillation Column
Controlled variables:
top composition
bottom composition